The AfriPop and AsiaPop projects provide freely available, detailed spatial data on population distributions in Africa and Asia to support epidemiological modeling and health metrics. The projects map people, pregnancies and births at a high resolution by linking census and satellite data. They estimate populations in grid cells, map age/gender demographics, and model accessibility to health services. Future work may involve mobile phone data, health worker mapping, and projecting population changes out to 2030. The goal is to improve understanding of population dynamics and access to services over time and space.
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The AfriPop and AsiaPop projects: Mapping people, pregnancies and births
1. The AfriPop and AsiaPop projects:
Mapping people, pregnancies and
births
Andy Tatem
University of Southampton
2. To discuss
โข Population mapping
โข Added value
โข What next?
โข Mash-up questions
3. Intro to gridded population data
Census data linked to GIS
administrative boundaries
Ancillary data e.g.
Settlements, roads
Spatial modeling rules to
disaggregate census counts
Estimates of number of
people in each grid cell
4. www.afripop.org www.asiapop.org
Aims: Build a database of freely-available, detailed and
contemporary spatial data on African/Asian population
distributions to support epidemiological modelling and
health metric derivation.
Initial focus:
1. Database of detailed, contemporary census data
2. Fine scale, accurate mapping of settlements
3. Sub-national mapping of age/gender structure
4. Low cost, easily updated
5. Satellite-derived
Census Admin
settlements/land Subnational, urban/rural
database boundaries
use growth rates
Population distributions
UN national estimate
adjustments
Sub-national
age/sex proportions Population
distributions
by age/sex Household
Admin boundaries Infrastructure
surveys:
, topography,
travel times,
land use data
Women of childbearing mode
Subnational, age: 5 yr groups
Friction Facility GPS
urban/rural age-
surface database
specific fertility rates
Births
Cost-distance model: travel
time estimates
Abortion, stillbirth
rates
Pregnancies Births, pregnancies, WOCBA
access to services
9. Redistributing census count data
โข 80-90% population covered through mapped
settlements
โข Remaining rural populations redistributed by
land cover specific weights
โข 5 countries with detailed census data
spanning range of ecological zones used to
derive empirical weights
10. >11,000 settlements with pop from: UN-OCHA provided population
United Nations Development Programme estimates by district for the year 2011
(UNDP), the German Agency for Technical
Cooperation (GTZ), the Kenya Medical
Research Institute (KEMRI), the Food Security
Analysis Unit (FSAU), and the UN Office for the
Coordination of Humanitarian Affairs (OCHA)
UN High Commission for Refugees (UNHCR)
refugee camp locations and sizes
Landsat derived settlement
extents 2005
15. Mapping population
demography
Distribution of children
under 5 yrs old in 2015
Source of subnational
age/sex data Proportion of the
population <5yrs old
17. Live births in 2010 per 100m
grid cell: 20-24 yr olds
Adjusted to match UN World
Population Prospects national total
estimates
18. Live births -> Pregnancies
Live births 2010
(UN-adjusted) Stillbirths = 3.6% of births
(http://www.who.int/pmnch/media/news/201
1/stillbirths_countryrates.pdf)
+ Abortions = 28 per 1000 women age 15-44
(http://www.guttmacher.org/pubs/journals/Se
dgh-Lancet-2012-01.pdf)
Pregnancies 2010
=
19. Pregnancies within X hours of
EmONC facilities
Pregnancies 2010
Travel time to
nearest health
facility
24. National estimates vs subnational
Liberia:
travel time to
nearest
health facility
Not accounting for subnational differences in demographic
composition can result in significant differences in metrics
26. Satellite-derived
Census Admin Regression Ancillary
settlements/land
database boundaries tree mapping data
use
Urban growth
Population distributions mapping/
simulation
Sub-national
age/sex proportions Population Dynamic population mapping
distributions
Admin boundaries by age/sex
Women of childbearing
Subnational, urban/r age: 5 yr groups
ural age-specific
fertility rates
Births
Bayesian model-based
Abortion, stillbirth geostatistical mapping
rates
Pregnancies
27. Population mapping: regression trees
โข Forest of regression trees โlearnsโ
pop density model weightings
โข Enables inclusion of a variety of
types of spatial dataset
โข Substantial accuracy improvements
29. Bayesian model-based geostatistics
โข Approach to exploit
increasing use of GPS in
national household surveys
โข Space-time models with
structured relationships with
covariates
โข Rigorous handling of
uncertainty
30. Dynamic population mapping
โข Mapping so far: Static annual average
residential populations
โข Reality: Regular travel, seasonal migration,
displaced populations
โข Redefine travel times/catchment areas/facility
network improvement beyond static pictures
โข Built on cutting edge data and methods
31. Mobile phone usage data
X
User makes a call Call routed through Network operator
from location X nearest tower records time and tower
of call for billing
Y
User travels to Y
and makes a call
32. Regular, local Seasonal Displacement Permanent
movements migration migration
Bharti, Tatem, Ferrari et al (2011) Science
33. The Mash-up
โข Subnational information on fertility rates,
stillbirths, abortions? (SAE / Geostats roles?)
โข Mapping health workers?
โข Models for projecting 10, 20 yrs ahead?
โข Comprehensive, accurate and contemporary
geolocated health facility datasets?
โข Quantify/map seasonal differences in access to
services?
โข Quantify/map rapidly changing population
distributions?
34. Acknowledgements Further information
www.afripop.org
www.asiapop.org
Catherine Linard, Andrea Gaughan, Forrest
Stevens, Zoe Matthews, Jim Campbell,
Pete Gething, Marius Gilbert, Dave Smith,
Amy Weslowski, Caroline Buckee, Carla
Pezzulo, Nita Bharti, Bryan Grenfell, Clara www.ameripop.org
Burgert
E-mail: A.J.Tatem@soton.ac.uk
Editor's Notes
-A summary intro for those who donโt know the background of how gridded pop data is generally produced and used
The main AfriPop aims.
First step = Assemble a database of detailed, contemporary census data.For some countries (about 1/3), more recent official estimations were used.We need to match administrative units, in the form of spatial polygons, with population data, which can be tricky.
This quickly demonstrates the detail of settlement mapping from Landsat โ links to next slideโฆ
Shows settlements for all malaria endemic countries.
Output comparisons GRUMP vs AfriPop
Maps show the original 100 m resolution dataset constructed using the methods described here. (A) Whole Africa database. (B) Close-up for a region in South-East Nigeria. (C) Close-up for the Khartoum area, Republic of the Sudan.Adjusted to 2010 using UN urban and rural growth rates
The full AsiaPop website was launched earlier this year.
Describe how a variety of subnational datasets on age and sex compositions are brought together, encompassing 1000s of administrative units to give a unique picture of age/sex patterns in Africa. The subnational proportions are then used to adjust AfriPop population maps to enable mapping of any male/female five year age group.
Phone data slide, access plot
Data anonymized and aggregated to ensure individuals cannot be identified